Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

The Machine Learning Job Market

evjang.com

Post reply on HN
Print view View original

261–270 of 276 posts

Re: The Machine Learning Job Market

#261

Post by albrewer »

awinter-py wrote: post #10

> The most important deciding factor for me was whether the company has some kind of technological edge years ahead of its competitors. A friend on Google’s logging team tells me he’s not interested in smaller companies because they are so technologically far behind Google’s planetary-scale infra that they haven’t even begun to fathom the problems that Google is solving now, much less finish solving the problems that…

> you can spend your whole project burn at the wrong company building something that you could buy somewhere else, or which already exists at a competitor

Oh look it's me. I'm sitting here building a worse version of TestComplete and Ranorex for automated testing of a Windows desktop application.

Looks good on my resume though.

Re: The Machine Learning Job Market

#262

Post by benibela »

hebrox wrote: post #249
benibela wrote: post #121

Earlier quoted context omitted.

That is why I am doing a Post Doc

May I ask what you're working on?

On causality

There is this interpretation of Bayesian networks that the parents are the true causes of a node and to predict what happens if you change a variable you need to remove the edges from the parents to that variable from the network. And then I study what you can do with that method

Re: The Machine Learning Job Market

#263

Post by max-hoffman »

falsenine wrote: post #135

As someone whose intention is to go to Medical School and pick up programming (+ math skills) to potentially work at the intersection of ML + Healthcare, the knowledge of the regulatory hurdles expressed is discouraging. Not sure if it really is worth the effort to study tech on top of medicine. Are there any people with experience within ML + Healthcare/Medicine or know of startups that are making great strides with…

When I was an MD/PhD student in 2017-2018, there were only a handful of labs specializing in applied medical ML and computational biology. Since leaving to work as an ML eng/backend eng, I have been surprised by how the relationships between academics, practitioners, and investors in pure software contribute to a positively reinforcing loop. On the medicine side, the academics lean towards software skepticism, the investors make fewer and safer bets to compensate for historically lower margins/growth/return multiples compared to pure software, and the subset of practitioners get payed orders of magnitude less and have correspondingly less engineering development. The differences affect everything from managerial quality, skill and career development, location flexibility, upward mobility, product scope and impact.

Re: The Machine Learning Job Market

#264

Post by NalNezumi »

PartiallyTyped wrote: post #238
captaincaveman wrote: post #169

Earlier quoted context omitted.

So central or eastern Europe ... yeah, thats gonna be tough!

No, actually in Scandinavia.

As a fellow Scandinavian I was in a similar position as you 2018, except that I was looking for MLE job with a (Software) Robotics background.(because beside ABB there were no robotics interest there).

Tl;dr I moved to Japan and worked in ML (ish) job. Once you start working it becomes remarkably easier to not be scoffed for lack of experience (even if you learned very little in that job)

Job security is high in Scandinavian countries and as an consequence people hire very risk-averse. risky/not so established jobs such as ML positions, they'll be actively looking for reasons NOT to hire you

Re: The Machine Learning Job Market

#265

Post by Nimitz14 »

angarg12 wrote: post #70

I don't want to derail the conversation, but OPs career path really stood out to me. He graduated in 2016, worked at Google in Bay Area, and now is joining a startup at a VP level. I graduated in 2008, obtained a PhD in 2014 in a no name EU university, worked in odd companies for a while and joined FAANG 4 years ago as a mid level developer, where I am still ATM. Looking at this disparity I wonder what could be possi…

Everyone starts from a different position. I don't think it's worth letting yourself get irritated or depressed by other's success. Just try change the position you're at to a better one.

and.. of course startups have title inflation ;)

Re: The Machine Learning Job Market

#266

Post by oneoff786 »

savant_penguin wrote: post #230
oneoff786 wrote: post #17

Earlier quoted context omitted.

On the other hand, there’s a lot of real problems that real people actually deal with that just need a logistic regression to save million bucks here and there. I like that space more.

Would you kindly tell what types of projects are such nice successes?

See adjacent responses

Re: The Machine Learning Job Market

#267

Post by cellis »

fxtentacle wrote: post #252
cellis wrote: post #204

Earlier quoted context omitted.

If you want to try to train a StyleGan for instance with image sizes of 1024 to acceptable quality, you need either a lot of GPUs or a lot of time, or both.

That's an oddly specific example, because I was in fact training my own StyleGAN and using it to sell T-Shirts around 2018 as indivicia.com . A GTX 1080 TI was good enough for 300 DPI A4 prints (roughly 3500px on the longer edge) and I just trained a regular low-res StyleGAN model and then a styled 4x upscaler. Execution was hand-coded multicore C++ and took about a second per user upload.

Very cool usage of StyleGan! I think it’s a great idea but maybe allow poster prints? I wouldn’t put a lot of these on a shirt but family pics or nature photos would be nice to have styleized.

But back to my original point: training stylegan takes a lot of resources at higher resolutions. For something like thispersondoesnotexist on stylegan 3 you not only need to have lots of quality data, you also need many cards for many days.

Re: The Machine Learning Job Market

#268

Post by rychco »

fxtentacle wrote: post #183
rychco wrote: post #65

Earlier quoted context omitted.

This has become increasingly important to me too. I am employed by a small (2-4 engineers at any time) company and I'm often disappointed because we're just so far behind in manpower & technical expertise that we have to dramatically reduce the scope of any problem we want to tackle. On the other hand, I also worry about getting sucked into the bureaucracy of FAANG sized companies & not having any accountability or a…

I'm surprised. Apart from DALLE, I haven't seen any AI approach that's off limits for 4 highly motivated people with 3090 GPUs. At that compute level, you should be able to at least replicate SOTA in optical flow, structure from motion, speech recognition, text to speech, translation, text summary, sentiment analysis, image classifications, image segmentation, and of course playing video games or optimizing processes…

Ah I wasn’t being clear, you’re actually correct when it comes to ML (which we use from time to time). My comment on the lack of manpower was mostly in regards to pure programming output or research efforts.

Re: The Machine Learning Job Market

#269

Post by samhw »

bsenftner wrote: post #139
samhw wrote: post #84

Earlier quoted context omitted.

> The innovation in AI really seems like it is being made on a thin line of engineering and compute. This perfectly echoes my own thoughts. The advances being trumpeted in AI are functions of hardware advances that allow us to have massively overparameterised models, models which essentially 'make the map the size of the territory'[0], which is why they only succeed at a narrow class of interpolation problems. And ev…

> show me a demo where it answers a hard question whose answer you - all of us - don't already know. For that, we need artificial comprehension, which we do not. Artificial comprehension, the ability to generalize systems to their base components and then virtually operate those base concepts to define what is possible, to virtual recreate physical working system, virtually improve them, and with those improvements b…

I'm not really sure what you mean. This seems to be another instance of the weirdly persistent belief that "only humans can understand, and computers are just moving gears around to mechanically simulate knowledge-informed action". I may not believe in the current NN-focussed AI hype cycle, but that's definitely not a cogent argument against the possibility of AI. You're confusing comprehension with the subjective (human) experience of comprehending something.

Re: The Machine Learning Job Market

#270

Post by samhw »

ChefboyOG wrote: post #134
samhw wrote: post #84

Earlier quoted context omitted.

> The innovation in AI really seems like it is being made on a thin line of engineering and compute. This perfectly echoes my own thoughts. The advances being trumpeted in AI are functions of hardware advances that allow us to have massively overparameterised models, models which essentially 'make the map the size of the territory'[0], which is why they only succeed at a narrow class of interpolation problems. And ev…

Eh, if you boil all research in AI/ML down to the binary of "AGI or bust," then sure, everything is a failure. But, if you look at your smartphone, virtually every popular application the average person uses--Gmail, Uber, Instagram, TikTok, Siri/Google Assistant, Netflix, your camera, and more--all owe huge pieces of their functionality to ML that's only become feasible in the last decade because of the research you'…

Sorry, I should have been clearer. I obviously concede that stuff like applying kNN over ginormous datasets to find TV shows people like, or doing some matrix decomposition to correlate ('recognise') objects in photographs, is obviously useful in the trivial sense. It has uses. It wouldn't exist otherwise. I was more thinking on a higher level, about whether it has led to any truly epochal technological advances, which it hasn't.

Machine learning / neural nets also (like I said) get to claim credit for a hell of a lot of things which are just products of colossal advances in hardware – simply of its becoming possible to run statistical methods over very very large '1:1 scale' sample sets – and not due to a specific statistical technique (NN) which is not remotely new and has been heavily researched for about 40-50 years now.

Post reply on HN